The Future of Education
In Times of Neural Machine Translation: Linguistic Competence Back on the Translation Stage
Gabriela Bulgaru, Babeș-Bolyai University (Romania)
Abstract
Long time ago, translation competence was seen as a mere linguistic asset, eventually a bilingual one. As society possibilities of communication developed and technology progressed, translation competence has been defined as a more and more complex set of knowledge and skills. It includes now an increasing number of sub-competences, depending on the perspective of approaching translation: either as a process, as a product, or as a profession. Nowadays machine translation and post-editing of automatic output, which have steadily entered the language service providers market, has brought a reorganization within the translation competence framework. In this paper we explore, thus, how a paradigmatic change has taken place: the linguistic sub-competence, which, as fundamental as it is, became rather shadowed by the other newer, modern sub-competences, is again at the core of translation competence. We bring in findings of our ongoing research on a corpus of machine-translated news texts, that we post-edited and quality assessed and evaluated, and discuss how machine translation fluency errors call for a refocus on linguistic competence in translation students’ training.
Keywords translation competence, linguistic sub-competence, machine translation post-editing, machine translation fluency errors
References
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